Edge-Enhanced Object-Space Model Optimization of Tomographic Reconstructions for Additive Manufacturing

Autor: Yanchao Zhang, Minzhe Liu, Hua Liu, Ce Gao, Zhongqing Jia, Ruizhan Zhai
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Micromachines, Vol 14, Iss 7, p 1362 (2023)
Druh dokumentu: article
ISSN: 2072-666X
DOI: 10.3390/mi14071362
Popis: Object-space model optimization (OSMO) has been proven to be a simple and high-accuracy approach for additive manufacturing of tomographic reconstructions compared with other approaches. In this paper, an improved OSMO algorithm is proposed in the context of OSMO. In addition to the two model optimization steps in each iteration of OSMO, another two steps are introduced: one step enhances the target regions’ in-part edges of the intermediate model, and the other step weakens the target regions’ out-of-part edges of the intermediate model to further improve the reconstruction accuracy of the target boundary. Accordingly, a new quality metric for volumetric printing, named ‘Edge Error’, is defined. Finally, reconstructions on diverse exemplary geometries show that all the quality metrics, such as VER, PW, IPDR, and Edge Error, of the new algorithm are significantly improved; thus, this improved OSMO approach achieves better performance in convergence and accuracy compared with OSMO.
Databáze: Directory of Open Access Journals